Nanometer-scale siRNA carriers incorporating peptidomimetic oligomers: physical characterization and biological activity
Bibliographic record
Abstract
Synthetic short interfering RNA (siRNA) oligonucleotides can trigger the RNA interference pathway and lead to selective gene silencing. Despite considerable enthusiasm and investment, formidable challenges remain that may deter translating this breakthrough discovery into clinical applications. In particular, the development of efficient, nontoxic, nonimmunogenic methods for delivering siRNA in vivo has proven to be exceptionally challenging. Thorough analysis of the relationship between the structure and function of siRNA carrier systems, both in isolation and in complex with RNA, will facilitate the design of efficient nonviral siRNA delivery vehicles. In this study, we explore the relationship between the physicochemical characteristics and the biological activity of "lipitoid" compounds as potent siRNA delivery vehicles. Lipitoids are cationic peptidomimetic oligomers incorporating a peptoid and a phospholipid moiety. Lipitoids can associate with siRNA oligonucleotides and self-assemble into spherical lipitoid-based nanoparticles (LNPs), with dimensions that are dependent upon the medium and the stoichiometric ratio between the cationic monomers of the lipitoid and anionic siRNA oligonucleotides. The morphology, gene silencing efficiency, and cytotoxicity of the siRNA-loaded LNPs are similarly sensitive to the stoichiometry of the complexes. The medium in which the LNPs are formed affects the assembled cargo particles' characteristics such as particle size, transfection efficiency, and stability. Formation of the LNPs in the biological, serum-free medium OptiMEM resulted in LNPs an order of magnitude larger than LNPs formed in water, and were twice as efficient in siRNA transfection compared to LNPs formed in water. Inhibitor studies were conducted to elucidate the efficiency of lysosomal escape and the uptake mechanism of the siRNA-loaded LNPs. Our results suggest that these lipitoid-based, siRNA-loaded spherical LNPs are internalized through a lipid raft-dependent and dynamin-mediated pathway, circumventing endosomal and lysosomal encapsulation. The lipitoid-siRNA nanospheres proved to be suitable platforms for investigating the critical parameters determining the efficiency of transfection agents, revealing the necessity for conducting characterization studies in biological media. The investigation of the LNP internalization pathway points to an alternative uptake route that bypasses the lysosome, explaining the surprisingly high efficiency of LNPs and suggesting that the uptake mechanism should be probed rather than assumed for the next generation of rationally designed transfection agents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".